New filtering algorithms for combinations of among constraints

  • Authors:

  • Willem-Jan Van Hoeve;Gilles Pesant;Louis-Martin Rousseau;Ashish Sabharwal

  • Affiliations:

  • Tepper School of Business, Carnegie Mellon University, Pittsburgh, USA 15213;École Polytechnique de Montréal, Montreal, Canada and CIRRELT, Université de Montréal, Montreal, Canada;École Polytechnique de Montréal, Montreal, Canada and CIRRELT, Université de Montréal, Montreal, Canada and Oméga Optimisation Inc., Montreal, Canada;Department of Computer Science, Cornell University, Ithaca, USA 14853

  • Venue:
  • Constraints
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Several combinatorial problems, such as car sequencing and rostering, feature sequence constraints, restricting the number of occurrences of certain values in every subsequence of a given length. We present three new filtering algorithms for the sequence constraint, including the first that establishes domain consistency in polynomial time. The filtering algorithms have complementary strengths: One borrows ideas from dynamic programming; another reformulates it as a regular constraint; the last is customized. The last two algorithms establish domain consistency, and the customized one does so in polynomial time. We provide experimental results that demonstrate the practical usefulness of each. We also show that the customized algorithm applies naturally to a generalized version of the sequence constraint that allows subsequences of varied lengths. The significant computational advantage of using a single generalized sequence constraint over a semantically equivalent collection of among or sequence constraints is demonstrated empirically.